{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:02.904187Z",
     "start_time": "2019-05-02T06:36:02.786348Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profiles.csv\ttest_queries.csv  train_plans.csv\r\n",
      "test_plans.csv\ttrain_clicks.csv  train_queries.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../Data/data_set_phase1/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:03.607279Z",
     "start_time": "2019-05-02T06:36:02.912581Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import warnings\n",
    "import time\n",
    "from collections import Counter\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "sns.set_style({'font.sans-serif': ['simsun', 'Arial']})\n",
    "sns.set_style('darkgrid', {'font.sans-serif': ['simhei', 'Arial']})\n",
    "%matplotlib inline\n",
    "\n",
    "main_path = r'../Data/data_set_phase1/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:04.179012Z",
     "start_time": "2019-05-02T06:36:03.609610Z"
    }
   },
   "outputs": [],
   "source": [
    "train_queries_df = pd.read_csv(main_path+'train_queries.csv')\n",
    "# train_queries_df = train_queries_df_copy.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:04.200107Z",
     "start_time": "2019-05-02T06:36:04.183146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>116.29,39.97</td>\n",
       "      <td>116.32,39.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3085857</td>\n",
       "      <td>210736.0</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>116.39,39.84</td>\n",
       "      <td>116.33,39.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2944522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>116.31,39.93</td>\n",
       "      <td>116.27,40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559931</td>\n",
       "      <td>202427.0</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>116.27,39.88</td>\n",
       "      <td>116.39,39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2819352</td>\n",
       "      <td>172251.0</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>116.34,39.96</td>\n",
       "      <td>116.37,39.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d\n",
       "0  3000821       NaN  2018-11-02 17:54:30  116.29,39.97  116.32,39.96\n",
       "1  3085857  210736.0  2018-11-16 10:53:10  116.39,39.84  116.33,39.79\n",
       "2  2944522       NaN  2018-10-06 10:33:58  116.31,39.93  116.27,40.00\n",
       "3   559931  202427.0  2018-11-23 14:54:11  116.27,39.88  116.39,39.90\n",
       "4  2819352  172251.0  2018-10-30 11:48:41  116.34,39.96  116.37,39.86"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T04:22:25.673577Z",
     "start_time": "2019-04-28T04:22:25.664143Z"
    }
   },
   "source": [
    "* sid: session ID [会话id]\n",
    "* pid: profile ID [用户id？]\n",
    "* req_time: request time [查询请求时间]\n",
    "* o: original point [出发地坐标]\n",
    "* d: destination point [目的地坐标]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:04.391049Z",
     "start_time": "2019-05-02T06:36:04.204393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid              0\n",
       "pid         163979\n",
       "req_time         0\n",
       "o                0\n",
       "d                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check null\n",
    "train_queries_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:04.402890Z",
     "start_time": "2019-05-02T06:36:04.395352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.327958"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "163979/train_queries_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:05.000517Z",
     "start_time": "2019-05-02T06:36:04.404595Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid         1.000000\n",
       "pid         0.092382\n",
       "req_time    0.928422\n",
       "o           0.010920\n",
       "d           0.009846\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries_df.nunique()/train_queries_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:05.053053Z",
     "start_time": "2019-05-02T06:36:05.002907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.37,39.86    5993\n",
       "116.32,39.89    5930\n",
       "116.42,39.90    5819\n",
       "116.41,39.91    5616\n",
       "116.37,39.91    5246\n",
       "116.40,39.91    5156\n",
       "116.39,39.90    4366\n",
       "116.35,39.94    3674\n",
       "116.39,39.91    3048\n",
       "116.41,39.90    3048\n",
       "Name: o, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries_df['o'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:05.186450Z",
     "start_time": "2019-05-02T06:36:05.055539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.32,39.89    16503\n",
       "116.37,39.86    13124\n",
       "116.42,39.90    11256\n",
       "116.39,39.90     7575\n",
       "116.39,39.91     7571\n",
       "116.41,39.91     6348\n",
       "116.37,39.91     5665\n",
       "116.45,39.93     5357\n",
       "116.27,40.00     4222\n",
       "116.40,39.93     3782\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries_df['d'].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "* pid缺失严重\n",
    "    * 讨论如何填补这个缺失值\n",
    "* sid为唯一值\n",
    "* 同一个出发地坐标或者目的坐标出现的次数多\n",
    "* 后期特征工程中需要看看req_time的时间分布情况，如：早晚高峰，节假日或者周末，周五等等。\n",
    "    * 处理方案是将时间戳划分出星期（标签型 or One-hot？）、小时（标签型 or One-hot？）、是否节假日（binary）等特征\n",
    "* 数据来自北京市用户出行的用户行为，因此在特征工程的时候是否需要考虑出发或者目的坐标的真实地理位置（市中心、商业集中地、景点等等），以此来加入堵车、人流量过高的真实现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:07.511333Z",
     "start_time": "2019-05-02T06:36:05.188753Z"
    }
   },
   "outputs": [],
   "source": [
    "train_plans_df = pd.read_csv(main_path+'train_plans.csv')\n",
    "# train_plans_df = train_plans_df_copy.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:07.523805Z",
     "start_time": "2019-05-02T06:36:07.513868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1709112</td>\n",
       "      <td>2018-11-04 11:45:04</td>\n",
       "      <td>[{\"distance\": 32303, \"price\": 600, \"eta\": 5087...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3327773</td>\n",
       "      <td>2018-10-16 19:09:29</td>\n",
       "      <td>[{\"distance\": 3427, \"price\": 300, \"eta\": 627, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3285959</td>\n",
       "      <td>2018-11-25 15:05:22</td>\n",
       "      <td>[{\"distance\": 36234, \"price\": 1100, \"eta\": 931...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1616170</td>\n",
       "      <td>2018-10-03 14:42:40</td>\n",
       "      <td>[{\"distance\": 11450, \"price\": 400, \"eta\": 2801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>351369</td>\n",
       "      <td>2018-10-05 19:52:37</td>\n",
       "      <td>[{\"distance\": 18495, \"price\": 600, \"eta\": 4271...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid            plan_time  \\\n",
       "0  1709112  2018-11-04 11:45:04   \n",
       "1  3327773  2018-10-16 19:09:29   \n",
       "2  3285959  2018-11-25 15:05:22   \n",
       "3  1616170  2018-10-03 14:42:40   \n",
       "4   351369  2018-10-05 19:52:37   \n",
       "\n",
       "                                               plans  \n",
       "0  [{\"distance\": 32303, \"price\": 600, \"eta\": 5087...  \n",
       "1  [{\"distance\": 3427, \"price\": 300, \"eta\": 627, ...  \n",
       "2  [{\"distance\": 36234, \"price\": 1100, \"eta\": 931...  \n",
       "3  [{\"distance\": 11450, \"price\": 400, \"eta\": 2801...  \n",
       "4  [{\"distance\": 18495, \"price\": 600, \"eta\": 4271...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* sid: session ID [会话id]\n",
    "* plan_time: timestamp [计划生成的时间？or 预计出行的具体时间？]\n",
    "* plans: list of route plans [计划列表]\n",
    "    * distance: the estimated route distance in meters [估计出行距离（米）]\n",
    "    * price: the estimated price in RMB cent [估计价格（人民币）]\n",
    "    * eta: the estimated time of arrival (ETA) in seconds [估计出行时间（秒）]\n",
    "    * mode: the transport mode [出行方式]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:07.689459Z",
     "start_time": "2019-05-02T06:36:07.526295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          0\n",
       "plan_time    0\n",
       "plans        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:08.794074Z",
     "start_time": "2019-05-02T06:36:07.691667Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          1.000000\n",
       "plan_time    0.927448\n",
       "plans        0.996964\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans_df.nunique()/train_plans_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "* 数据无缺失\n",
    "* 数据重复数量也不高\n",
    "* 需要根据sid结合quries表继续探索req_time和plan_time的关系\n",
    "* 时间处理过程中是否加入出行过程中的时间跨度，比如下午16点出行不是晚高峰，但是结合ETA（估计出行时间的特征），可能会在途中遇到晚高峰的情况\n",
    "    * 解决方案可以在plan_time这个特征上加上ETA，计算出到达目的地的具体时间，再做特征工程。前提是弄清楚req_time和plan_time之间的关系\n",
    "* plans得再unstack成新的表，统计每个会话里面的最大（最小、平均）价格、最长（最小、平均）出行时间、出行方式出现次数等等，具体得讨论。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train clicks\n",
    "这个应该是label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:09.129341Z",
     "start_time": "2019-05-02T06:36:08.796529Z"
    }
   },
   "outputs": [],
   "source": [
    "train_clicks_df = pd.read_csv(main_path+'train_clicks.csv')\n",
    "# train_clicks_df = train_clicks_df_copy.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:09.145046Z",
     "start_time": "2019-05-02T06:36:09.131916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>click_time</th>\n",
       "      <th>click_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2848914</td>\n",
       "      <td>2018-11-17 18:42:17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2629085</td>\n",
       "      <td>2018-10-12 16:28:13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>602598</td>\n",
       "      <td>2018-11-11 16:38:42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022975</td>\n",
       "      <td>2018-10-14 12:28:11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>988425</td>\n",
       "      <td>2018-11-12 16:47:30</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid           click_time  click_mode\n",
       "0  2848914  2018-11-17 18:42:17           1\n",
       "1  2629085  2018-10-12 16:28:13           3\n",
       "2   602598  2018-11-11 16:38:42           2\n",
       "3  2022975  2018-10-14 12:28:11           9\n",
       "4   988425  2018-11-12 16:47:30           7"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A click record indicates the user feedback of different recommendations. i.e., a user may click on specific routes displayed to him/her for details.**\n",
    "* sid: session ID [会话时间]\n",
    "* click_time: a timestamp [点击时间戳]\n",
    "* click_mode: the clicked transport mode in the display list [用户选择的出行方式]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:09.268677Z",
     "start_time": "2019-05-02T06:36:09.147899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           0\n",
       "click_time    0\n",
       "click_mode    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:09.679592Z",
     "start_time": "2019-05-02T06:36:09.271760Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           1.000000\n",
       "click_time    0.931097\n",
       "click_mode    0.000024\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks_df.nunique()/train_clicks_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:09.708836Z",
     "start_time": "2019-05-02T06:36:09.688968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136491</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>78209</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70369</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48864</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47480</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24626</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14882</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12606</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11863</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6089</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1857</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frequency  Mode\n",
       "2      136491     2\n",
       "7       78209     7\n",
       "1       70369     1\n",
       "9       48864     9\n",
       "5       47480     5\n",
       "3       24626     3\n",
       "10      14882    10\n",
       "4       12606     4\n",
       "6       11863     6\n",
       "11       6089    11\n",
       "8        1857     8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode_df = pd.DataFrame(train_clicks_df.click_mode.value_counts())\n",
    "mode_df['mode'] = mode_df.index\n",
    "mode_df.columns = ['Frequency', 'Mode']\n",
    "mode_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.176881Z",
     "start_time": "2019-05-02T06:36:09.716758Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_clicks_df.click_mode.value_counts()\n",
    "f, ax = plt.subplots(figsize=(20, 15))\n",
    "g = sns.barplot(x='Mode', y='Frequency', data=mode_df, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.461407Z",
     "start_time": "2019-05-02T06:36:10.179101Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart if labels\n",
    "mpl.rcParams['font.size'] = 20\n",
    "f1, ax1 = plt.subplots(figsize=(20, 15))\n",
    "ax1.pie(mode_df['Frequency'].values,\n",
    "        labels=mode_df['Mode'].values, autopct='%1.1f%%', shadow=True)\n",
    "ax1.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T05:10:20.362995Z",
     "start_time": "2019-04-28T05:10:20.354229Z"
    }
   },
   "source": [
    "### 小结\n",
    "* label不平衡\n",
    "    * 后面得想办法处理（加权采样、组合label、模型加权输出等等）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.468487Z",
     "start_time": "2019-05-02T06:36:10.463818Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train qureies shape: (500000, 5)\n",
      "train plans shape: (491054, 3)\n",
      "train clicks shape: (453336, 3)\n"
     ]
    }
   ],
   "source": [
    "print('train qureies shape:', train_queries_df.shape)\n",
    "print('train plans shape:', train_plans_df.shape)\n",
    "print('train clicks shape:', train_clicks_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 并不是所有的queries都会有对应的plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.652435Z",
     "start_time": "2019-05-02T06:36:10.475372Z"
    }
   },
   "outputs": [],
   "source": [
    "test_queries_df = pd.read_csv(main_path+'test_queries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.666122Z",
     "start_time": "2019-05-02T06:36:10.654891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1126541</td>\n",
       "      <td>178395.0</td>\n",
       "      <td>2018-12-05 14:51:43</td>\n",
       "      <td>116.41,39.92</td>\n",
       "      <td>116.46,39.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453685</td>\n",
       "      <td>179178.0</td>\n",
       "      <td>2018-12-01 12:50:33</td>\n",
       "      <td>116.39,39.90</td>\n",
       "      <td>116.41,39.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2066073</td>\n",
       "      <td>184748.0</td>\n",
       "      <td>2018-12-07 10:17:35</td>\n",
       "      <td>116.37,39.71</td>\n",
       "      <td>116.31,39.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452746</td>\n",
       "      <td>162795.0</td>\n",
       "      <td>2018-12-01 07:52:44</td>\n",
       "      <td>116.33,39.97</td>\n",
       "      <td>116.31,39.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1431672</td>\n",
       "      <td>116384.0</td>\n",
       "      <td>2018-12-06 10:36:30</td>\n",
       "      <td>116.51,39.68</td>\n",
       "      <td>116.51,39.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d\n",
       "0  1126541  178395.0  2018-12-05 14:51:43  116.41,39.92  116.46,39.95\n",
       "1   453685  179178.0  2018-12-01 12:50:33  116.39,39.90  116.41,39.91\n",
       "2  2066073  184748.0  2018-12-07 10:17:35  116.37,39.71  116.31,39.89\n",
       "3   452746  162795.0  2018-12-01 07:52:44  116.33,39.97  116.31,39.98\n",
       "4  1431672  116384.0  2018-12-06 10:36:30  116.51,39.68  116.51,39.68"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.762068Z",
     "start_time": "2019-05-02T06:36:10.668766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid             0\n",
       "pid         31447\n",
       "req_time        0\n",
       "o               0\n",
       "d               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check null\n",
    "test_queries_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.799364Z",
     "start_time": "2019-05-02T06:36:10.766819Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失占比： 0.3332732783653744\n"
     ]
    }
   ],
   "source": [
    "print('缺失占比：',31447/test_queries_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:10.947244Z",
     "start_time": "2019-05-02T06:36:10.803803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid         1.000000\n",
       "pid         0.140942\n",
       "req_time    0.891880\n",
       "o           0.034517\n",
       "d           0.030564\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries_df.nunique()/test_queries_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "* 和train_queries情况基本一致"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## test plan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:11.343837Z",
     "start_time": "2019-05-02T06:36:10.951236Z"
    }
   },
   "outputs": [],
   "source": [
    "test_plans_df = pd.read_csv(main_path+'test_plans.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:11.356468Z",
     "start_time": "2019-05-02T06:36:11.346338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112456</td>\n",
       "      <td>2018-12-05 17:39:47</td>\n",
       "      <td>[{\"distance\": 465, \"price\": \"\", \"eta\": 418, \"t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1413458</td>\n",
       "      <td>2018-12-06 17:49:17</td>\n",
       "      <td>[{\"distance\": 28846, \"price\": 600, \"eta\": 4779...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1243160</td>\n",
       "      <td>2018-12-02 14:50:25</td>\n",
       "      <td>[{\"distance\": 24646, \"price\": 600, \"eta\": 3513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2040494</td>\n",
       "      <td>2018-12-07 09:23:45</td>\n",
       "      <td>[{\"distance\": 32235, \"price\": 600, \"eta\": 4072...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1448779</td>\n",
       "      <td>2018-12-06 18:24:46</td>\n",
       "      <td>[{\"distance\": 13502, \"price\": 400, \"eta\": 3195...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid            plan_time  \\\n",
       "0  1112456  2018-12-05 17:39:47   \n",
       "1  1413458  2018-12-06 17:49:17   \n",
       "2  1243160  2018-12-02 14:50:25   \n",
       "3  2040494  2018-12-07 09:23:45   \n",
       "4  1448779  2018-12-06 18:24:46   \n",
       "\n",
       "                                               plans  \n",
       "0  [{\"distance\": 465, \"price\": \"\", \"eta\": 418, \"t...  \n",
       "1  [{\"distance\": 28846, \"price\": 600, \"eta\": 4779...  \n",
       "2  [{\"distance\": 24646, \"price\": 600, \"eta\": 3513...  \n",
       "3  [{\"distance\": 32235, \"price\": 600, \"eta\": 4072...  \n",
       "4  [{\"distance\": 13502, \"price\": 400, \"eta\": 3195...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:11.448020Z",
     "start_time": "2019-05-02T06:36:11.359736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          0\n",
       "plan_time    0\n",
       "plans        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:11.655962Z",
     "start_time": "2019-05-02T06:36:11.453623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          1.000000\n",
       "plan_time    0.890473\n",
       "plans        0.995593\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans_df.nunique()/test_plans_df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "* 同train_plans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:12.090235Z",
     "start_time": "2019-05-02T06:36:11.658850Z"
    }
   },
   "outputs": [],
   "source": [
    "profiles_df = pd.read_csv(main_path+'profiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:12.121450Z",
     "start_time": "2019-05-02T06:36:12.092520Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>p0</th>\n",
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       "      <th>p6</th>\n",
       "      <th>p7</th>\n",
       "      <th>p8</th>\n",
       "      <th>...</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>204083</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170667</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>129719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pid   p0   p1   p2   p3   p4   p5   p6   p7   p8  ...  p56  p57  p58  \\\n",
       "0  196356  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "1  204083  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  ...  0.0  0.0  0.0   \n",
       "2  170667  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  ...  0.0  0.0  0.0   \n",
       "3  115511  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "4  129719  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "   p59  p60  p61  p62  p63  p64  p65  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1  1.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:12.283567Z",
     "start_time": "2019-05-02T06:36:12.124255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 63090 entries, 0 to 63089\n",
      "Data columns (total 67 columns):\n",
      "pid    63090 non-null int64\n",
      "p0     63090 non-null float64\n",
      "p1     63090 non-null float64\n",
      "p2     63090 non-null float64\n",
      "p3     63090 non-null float64\n",
      "p4     63090 non-null float64\n",
      "p5     63090 non-null float64\n",
      "p6     63090 non-null float64\n",
      "p7     63090 non-null float64\n",
      "p8     63090 non-null float64\n",
      "p9     63090 non-null float64\n",
      "p10    63090 non-null float64\n",
      "p11    63090 non-null float64\n",
      "p12    63090 non-null float64\n",
      "p13    63090 non-null float64\n",
      "p14    63090 non-null float64\n",
      "p15    63090 non-null float64\n",
      "p16    63090 non-null float64\n",
      "p17    63090 non-null float64\n",
      "p18    63090 non-null float64\n",
      "p19    63090 non-null float64\n",
      "p20    63090 non-null float64\n",
      "p21    63090 non-null float64\n",
      "p22    63090 non-null float64\n",
      "p23    63090 non-null float64\n",
      "p24    63090 non-null float64\n",
      "p25    63090 non-null float64\n",
      "p26    63090 non-null float64\n",
      "p27    63090 non-null float64\n",
      "p28    63090 non-null float64\n",
      "p29    63090 non-null float64\n",
      "p30    63090 non-null float64\n",
      "p31    63090 non-null float64\n",
      "p32    63090 non-null float64\n",
      "p33    63090 non-null float64\n",
      "p34    63090 non-null float64\n",
      "p35    63090 non-null float64\n",
      "p36    63090 non-null float64\n",
      "p37    63090 non-null float64\n",
      "p38    63090 non-null float64\n",
      "p39    63090 non-null float64\n",
      "p40    63090 non-null float64\n",
      "p41    63090 non-null float64\n",
      "p42    63090 non-null float64\n",
      "p43    63090 non-null float64\n",
      "p44    63090 non-null float64\n",
      "p45    63090 non-null float64\n",
      "p46    63090 non-null float64\n",
      "p47    63090 non-null float64\n",
      "p48    63090 non-null float64\n",
      "p49    63090 non-null float64\n",
      "p50    63090 non-null float64\n",
      "p51    63090 non-null float64\n",
      "p52    63090 non-null float64\n",
      "p53    63090 non-null float64\n",
      "p54    63090 non-null float64\n",
      "p55    63090 non-null float64\n",
      "p56    63090 non-null float64\n",
      "p57    63090 non-null float64\n",
      "p58    63090 non-null float64\n",
      "p59    63090 non-null float64\n",
      "p60    63090 non-null float64\n",
      "p61    63090 non-null float64\n",
      "p62    63090 non-null float64\n",
      "p63    63090 non-null float64\n",
      "p64    63090 non-null float64\n",
      "p65    63090 non-null float64\n",
      "dtypes: float64(66), int64(1)\n",
      "memory usage: 32.2 MB\n"
     ]
    }
   ],
   "source": [
    "profiles_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 不存在缺失的情况\n",
    "* pid的是唯一的;\n",
    "* p0-p65都是二元的;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:36:12.290203Z",
     "start_time": "2019-05-02T06:36:12.285953Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test qureies shape: (94358, 5)\n",
      "test plans shape: (92571, 3)\n",
      "profiles shape: (63090, 67)\n"
     ]
    }
   ],
   "source": [
    "print('test qureies shape:', test_queries_df.shape)\n",
    "print('test plans shape:', test_plans_df.shape)\n",
    "print('profiles shape:', profiles_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* test_plans和test_queries数据不对等，和train数据情况一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:39:02.424021Z",
     "start_time": "2019-05-02T06:39:02.397339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167770.0    6094\n",
       "112198.0    4856\n",
       "116844.0    3187\n",
       "203487.0    2818\n",
       "186452.0    2465\n",
       "Name: pid, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries_df['pid'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:39:08.560947Z",
     "start_time": "2019-05-02T06:39:08.542940Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167770.0    1635\n",
       "112198.0    1308\n",
       "203487.0     714\n",
       "116844.0     676\n",
       "202427.0     664\n",
       "Name: pid, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries_df['pid'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-02T06:39:34.637296Z",
     "start_time": "2019-05-02T06:39:34.629250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63090, 67)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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